2 positions are analysts, modelers, data scientists, analytics consultants and leaders working in analytics or IT teams and departments. The courses starting with code 3 are "Application" track courses and aims to present and describe business applications of analytics and targets practitioners with a level of business experience. Typical positions targeted are analysts, specialists, managers and executives of related departments. The courses starting with code 4 are "Strategy and Management" track courses targeting executive professionals and leaders who are responsible for managing and organizing analytics projects, initiatives, functions, or organizations. Course Descriptions 1 - Math and Theory Track Fundamentals of Business Analytics The course covers main topics in the fields of analytics, statistics, data mining, machine learning and aims to build a theoretical and mathematical foundation. The following are some of the topics to be covered: Overview of linear algebra and statistical mathematics Concepts of business statistics and data mining Exploratory data analysis and data preparation Multivariate Statistical Methods Analysis of Variance (ANOVA) Discriminant analysis Regression analysis Structural equation modeling Principal component and factor analysis Multi dimensional Scaling Conjoint and correspondence analysis Design of experiments Time series analysis and forecasting Data mining methods and algorithms Descriptive methods: Clustering, association rules Predictive methods: Decision trees, logistic regression, support vector machines, neural networks. Advanced modeling approaches Model performance evaluation and model maintenance.

3 102 - Data- driven Decision Making The course covers topics from prescriptive analytics, operations research, system simulation and decision theory fields to provide a background on quantitative modeling. Main topics to be covered are: Mathematical programming and modeling Prescriptive analytics Decision systems Operations research and optimization Business and process simulation 2 - Tools and Software Track Data Engineering Introductory Data Analysis with Spreadsheets and Databases The course provides a basic training to develop skills for effective usage of spreadsheets and simple usage of database programs for quick data analysis. Working with spreadsheets (MS Excel) Working with databases (MS Access, MySQL etc.) Management Information and Data Systems The course aims to provide an understanding of systems, components and functions of information and data systems used in practice and fundamental skills for efficiently using these systems. Some of the topics to be covered are: Information and data systems Concepts of information and data Relational databases and SQL Data warehouses, OLAP NOSQL systems Data handling Accessing, Querying, Reporting ETL processes Data quality and cleansing Data visualization

4 203 - Data Analytics with Python with Python language, packages and libraries Data Analytics with R with Cran- R environment, language and packages Data Analytics with SAS with the SAS System Data Analytics with IBM Modeler (SPSS) with the IBM Modeler (SPSS) software. Mastering Big Data High Performance Analytics (Big Data) This course covers topics from high performance analytics and big data paradigms and aims to provide an hands- on introduction to emerging technologies in the field. Some of the topics to be covered are: Big data and high performance concepts Architectures, components and tools

7 Strategy and Management Track 401- Big Data for Executives and Leaders This course aims to provide an overview of Big Data applications, best practices and ideas on how Big Data paradigm can help executives to transform their businesses Management and Organization of Analytics Practices and Initiatives This course aims to provide background, tips and tricks on how to successfully define, organize, manage and monitor practices involving analytics content and teams. The course presents how to successfully formulate and design strategies, organize analytics teams and functions, deploy efficient processes for successful analytics practices in enterprises.

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